Premium
ASCA: analysis of multivariate data obtained from an experimental design
Author(s) -
Jansen Jeroen J.,
Hoefsloot Huub C. J.,
van der Greef Jan,
Timmerman Marieke E.,
Westerhuis Johan A.,
Smilde Age K.
Publication year - 2005
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.952
Subject(s) - multivariate statistics , multivariate analysis , multivariate analysis of variance , analysis of variance , computer science , component analysis , variance (accounting) , data analysis , regression analysis , statistics , data mining , mathematics , artificial intelligence , machine learning , business , accounting
Recently analysis of variance (ANOVA)‐simultaneous component analysis (ASCA) has been introduced as an explorative tool for the analysis of multivariate data with an underlying experimental design [Smilde et al. Bioinformatics 2005; 21: 3043–3048]. This paper focuses on the general methodological framework of ASCA. The drawbacks of other methods for the analysis of this type of data are discussed, as well as the advantages of ASCA above these other methods. Three case studies are used to illustrate the use of ASCA. The relationship between ASCA and several other multivariate data analysis techniques is demonstrated. Finally, possible extensions for ASCA are presented, including multiway analysis and multivariate regression. Copyright © 2006 John Wiley & Sons, Ltd.